Decision making and forecasting in the present world depend enormously on large volumes of data. There are multiple approaches to analyze large or small amounts of data in which the graphical approach of inspecting data is the most popular one. Charts and maps are the graphical depiction of data, in which the data is represented by symbols, bars in a bar chart, lines in a line chart and slices in a pie chart. In general, a chart is a representation of the rules associated with the data and its corresponding numerical values.
In order to visualize large or small amounts of data various efforts have been made in the past. Many data analyzing tools and web based platforms are available, such as Microsoft Office-Excel®, Apple iWork® suite, Google Analytics, SAS Analytics, SAP Analytics, Adobe Analytics, IBM Analytics etc. Many of the tools are expensive, require highly trained people in the tool and are time consuming. Many others require the data to be structured in the same way as a database table in fixed rows and columns. Products like Microsoft Excel® analyze fixed data formats, primarily in tabular format. Some of the tools need to import the data from a source (e.g. Excel®) into their product before it can be worked upon.
There are also tools such as Google Docs Sheets® that require a selection of cells with data to be included in the chart or alternatively, selection of a range or multiple ranges of data from within the chart editor. In order to do so one has to click “Select Range” and enter one or more ranges by clicking “Add Another Range”. Further, one has to select the “Chart” icon in the menu bar and then the chart editor box appears.
Similarly, in Apple iWorks® to draw a chart, first one has to select the table cells for reference, and then has to hold down the Option key by clicking Charts in the toolbar. Further, one has to choose a chart type. When the pointer changes into a crosshair, one has to drag the crosshair across the canvas to create a chart of the required size. Further to limit the chart's proportions, dragging is needed while holding down the shift key.
Thus, most of the available tools are complicated for a layman and generally require highly trained people to perform the process and even to choose appropriate graph types and ranges.
Further, U.S. Pat. No. 8,423,567 entitled “dynamic query data visualizer” discusses a method that classifies query as numerical, date, or text according to their field type, executes and displays results in a first and a second dimension of the interface depending on the selections made by the user. Also, the U.S. Pat. No. 6,057,837 entitled “On-screen identification and manipulation of sources that an object depends upon” depicts a method for identification and manipulation of a range of spreadsheet cells that are referred to by spreadsheet cell formulas or define graphs. However none of above mentioned systems or processes provides the method for the interaction of cells of two or more worksheets to follow formulas and rules.
Also, none of the above mentioned systems or processes disclose any system and/or method for dynamic generation of graph for plurality of data sets. In presently available tools, it is required to repeat the whole process for analyzing other sets of data.
Further, the processes of observing, measuring, interpreting, classifying, and analyzing data give rise to systematic and random errors. Some errors may be quite large and easily detectable. Other errors and uncertainties in data are more subtle and are not easily detected or evaluated. Users of datasets may not be aware of their presence or even the possibility of their existence. Graphical methods in conjunction with error analysis provide a means to illuminate obvious and more subtle errors and evaluating the uncertainty in data sets.
Humans have strong acuity for visualization with an exceptional ability to recognize structure and relationships. Representing information in a form that matches human perceptual capabilities makes the process of getting information and digesting it easier and more effective. In presently available tools, such as R, MATLAB®, Tableau's Software®, Microsoft's Excel®, it is required to evaluate the data errors from a statistical viewpoint or else just display the data and allow the user to visually imply an error or anomaly in the sets of data.
Additionally, available tools which focus on graphical method of statistics do not allow for methods based on empirical trends in data based on the label or position in the dataset relative to its peers.
Hence, in light of the discussion above, it is desirable to devise a dynamic graph generating tool that could bring data to life and a dynamic graphical error detection technology based on rules associated with the real world application of the data that could demonstrate empirical data errors and could overcome one or more problems and disadvantages associated with conventional systems or processes.